JohnWilliams_Module15_Main

Final Project - John Williams

Load libraries

library(tidyverse)
library(raster)          #raster()
library(sf)              #st_read()
library(ggspatial)       #annotation_scale,annotation_north_arrow
library(ggnewscale)      #new_scale_color() 
library(ggsn)            #scalebar()
library(shiny)           #Shiny app
library(plotly)          #plot_ly()
library(gridExtra)       #grid.arrange()

Set working directory

setwd("~/FinalProject")

Read the data

##################Read data file into R
load(file = "CleaneData.Rdata")

Join Tables

########Join the rational tables and check the missing values
CS_Erate<-right_join(Contiguous_state, Unemployrate, 
                     by = c("STUSPS"))

CS_Erate_Crate <- right_join(CS_Erate, Crimerate, 
                             by = c("STUSPS","Year"))
CS_Erate_Crate1 <- CS_Erate_Crate %>% 
  dplyr::select(REGION,STUSPS,NAME,Year,Meanrate,Crimerate) %>% 
  dplyr::rename("Unemplyrate"="Meanrate")

Create Plots

Prep for plots

## Perform the filter for the Year, preserving geometry only from CS_Erate_Crate1
CS_Erate_Crate2 <- CS_Erate_Crate1 %>%
  filter(Year == 2014)

Plot 1

# Plot 1 The unemployment spatial map in 2014 over contiguous USA
CS_Erate_Crate2 %>%
  ggplot() +
  geom_sf(aes(fill = Unemplyrate), color = "white", size = 0.3) +
  scale_fill_gradientn(colors = c("darkblue", "lightblue"), name = "Year2014-Unemployment Rate",
                       limits = c(3, 10)) +
  labs(title = "Unemployment Rate Map Over Contiguous US") +
  #scale bar
  scalebar(data=CS_Erate_Crate2, location = "bottomleft", anchor = c(x=-125, y=25), dist = 1000,
           dist_unit="km", transform=TRUE, model="WGS84", st.dist=0.04)+
  ## Add North Arrow
  annotation_north_arrow(location="br", which_north="true",
                         # pad_x = unit(2.5, "in"), pad_y = unit(0.2, "in"),
                         style = north_arrow_fancy_orienteering) +
  theme(legend.position="right",       #change 'top' to specific position by c(2.5,0.8)
        plot.title = element_text(hjust = 0.5,
                                  color = "Gray40",
                                  size = 16,
                                  face = "bold"),
        plot.subtitle = element_text(color="blue"),
        plot.caption = element_text(color="Gray60")) +
  xlab("Longitude") + ylab("Latitude")

Plot 2

#Plot 2 is the crimerate spatial map in 2014 over contiguous USA
CS_Erate_Crate2 %>%
  ggplot() +
  geom_sf(aes(fill = Crimerate), color = "white", size = 0.3) +
  scale_fill_gradientn(colors = c("darkblue", "lightblue"), name = "Year2014-Crime Rate",
                       limits = c(.1, .6)) +
  labs(title = "Crime Rate Map Over Contiguous US") +
  #scale bar
  scalebar(data=CS_Erate_Crate2, location = "bottomleft", anchor = c(x=-125, y=25), dist = 1000,
           dist_unit="km", transform=TRUE, model="WGS84", st.dist=0.04)+
  ## Add North Arrow
  annotation_north_arrow(location="br", which_north="true",
                         # pad_x = unit(2.5, "in"), pad_y = unit(0.2, "in"),
                         style = north_arrow_fancy_orienteering) +
  theme(legend.position="right",       #change 'top' to specific position by c(2.5,0.8)
        plot.title = element_text(hjust = 0.5,
                                  color = "Gray40",
                                  size = 16,
                                  face = "bold"),
        plot.subtitle = element_text(color="blue"),
        plot.caption = element_text(color="Gray60")) +
  xlab("Longitude") + ylab("Latitude")

Plot 3

## Plot 3 The time series visualization of Unemployment for some states
states_to_plot <- c("California", "Idaho", "Illinois", "Indiana")
CS_Erate_Crate1$geometry <- NULL      #Drop geometry column for plotly

# Filter data for the specified states and years
ts_data <- CS_Erate_Crate1 %>%
  filter(Year >= 2007, Year <= 2014, NAME %in% states_to_plot)

# Create the plotly time series plot
plot_ly(data = ts_data,
  x = ~Year,
  y = ~Unemplyrate,
  color = ~NAME,
  type = 'scatter',         # Set type to scatter for lines and markers
  mode = 'lines+markers',   # Use lines and markers
  text = ~paste("Year:", Year, "<br>Unemployment Rate:", Unemplyrate),
  hoverinfo = 'text'        # Show tooltip text
) %>%
  layout(
    title = "Unemployment Rate Changes Along with Years",  # Title
    xaxis = list(title = "Year", tickvals = 2007:2014),    # x-axis label and ticks
    yaxis = list(title = "Unemployment Rate", range = c(3, 14)),  # y-axis label and range
    legend = list(title = list(text = ""))  # Remove legend title
  )

Plot 4

#Plot 4 The time series visualization of Crimerate for some states
# Define states to plot
states_to_plot <- c("California", "Idaho", "Florida", "Indiana")

# Filter data for the specified states and years
ts1_data <- CS_Erate_Crate1 %>%
  filter(Year >= 2007, Year <= 2014, NAME %in% states_to_plot)

# Create the plotly time series plot
plot_ly(data = ts1_data,
  x = ~Year,
  y = ~Crimerate,
  color = ~NAME,
  type = 'scatter',         # Set type to scatter for lines and markers
  mode = 'lines+markers',   # Use lines and markers
  text = ~paste("Year:", Year, "<br>Crime Rate:", Crimerate),
  hoverinfo = 'text'        # Show tooltip text
) %>%
  layout(
    title = "Crime Rate Changes Along with Years",  # Title
    xaxis = list(title = "Year", tickvals = 2007:2014),    # x-axis label and ticks
    yaxis = list(title = "Crime Rate", range = c(.1, .8)),  # y-axis label and range
    legend = list(title = list(text = ""))  # Remove legend title
  )

Plot 5

## Plot 5 Scatter Plot with colors by Region
# Create a plot with colors by Region
CS_Erate_Crate2$geometry <- NULL  #Drop geometry column for plotly

plot_ly(
  data = CS_Erate_Crate2,
  x = ~Crimerate,
  y = ~Unemplyrate,
  type = 'scatter',
  mode = 'markers',
  color = ~as.factor(REGION)
) %>%
  layout(
    title = "Unemployment Rate and Crime Rate in 2014",
    xaxis = list(title = "Crime Rate per 100 people"),
    yaxis = list(title = "UnemploymentRates per 100 people")
  )